Well Log Channel Matching Using Hash Values and Depth Offset
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Solution Overview
Problem
Well logs with missing metadata can become 'unknown' channels, lacking contextual information, which complicates matching and analysis in well log channel data, leading to inefficiencies in data interpretation and operational decisions.
Innovation Solution
A method involving calculating hash values for well log channels, extracting snippets, and identifying matching channels by searching for hash value matches, with the depth offset delta used to score and determine the best match, thereby restoring channel context for unknown channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional well log channel matching methods are used, then matching accuracy can be maintained, but processing time and computational resources increase significantly due to large data volumes
Solution Approach 1:
The patent divides the well log channel matching process into two stages: first computing hash values for all channels during an offline preprocessing phase and storing them in a lookup table, then performing rapid online matching by comparing hash values of unknown channels against the precomputed table. This segmentation separates the computationally intensive hashing operation from the time-critical matching operation, enabling fast processing while maintaining accuracy.
Solution Approach 2:
The patent performs the computationally expensive hash value computation in advance during an offline preprocessing stage, before the actual matching is needed. By precomputing and storing hash values for known channels in a lookup table, the system eliminates the need to perform repeated hashing during online matching operations, significantly reducing processing time while preserving matching accuracy through the use of depth offset delta calculations.
2Measurement precision
If comprehensive channel context information is stored for matching, then matching accuracy improves, but data storage requirements and system complexity increase
Solution Approach 1:
The patent extracts only the essential identifying feature (hash value) from the complete channel context information and stores it in a compact lookup table. By separating the hash value extraction from the full channel data and using the hash as a unique identifier for matching, the system achieves accurate channel identification while minimizing storage requirements and system complexity. The full channel context is retained only when needed for detailed analysis.
3Productivity
If hash value matching is used for fast processing, then processing speed improves, but risk of hash collisions and false matches increases
Solution Approach 1:
The patent incorporates a feedback mechanism that calculates the depth offset delta between matching channels and uses this information to verify match validity. When hash values match, the system computes the depth offset and checks whether it falls within acceptable ranges, providing a verification step that eliminates false positives from hash collisions. This feedback loop maintains high processing speed while ensuring matching reliability through empirical validation of match results.
Data Source
AI summary
A method may include calculating a first set of hash values for a set of well log channels, extracting a well log channel snippet from an unknown well log channel, calculating a second set of hash values for the well log channel snippet, identifying, for the unknown well log channel, a matching well log channel by searching the first set of hash values with the second set of hash values, and storing, for the unknown well log channel, a channel context corresponding to the matching well log channel.